Electroencephalography (EEG) and magnetoencephalogray (MEG) are non-invasive techniques used to study neural activities (Cooper et al. 1980; Genvins 1998; He and Lian 2002). One important application of EEG and MEG is source localization, i.e., the determination of locations of electrical activity in the brain from the EEG or MEG signals. Such source mapping plays an important role in localizing the origin(s) of neurological disorders such as epilepsy.
A problem in localizing sources is that a unique relationship may not exist between the recorded EEG or MEG signals and the neural source(s). Therefore, different source localization models have been created. One category of source models uses equivalent current dipole models to represent well-localized activated neural sources (Wood 1982; Scherg and von Cramon 1985; He et al. 1987; Mosher et al. 1992; Cuffin 1995).
Among the dipole source localization algorithms are the subspace-based methods (see Mosher et al. 1992; and Mosher and Leahy 1999). In principle, subspace-based methods find peak locations of their cost functions as source locations by employing certain projections onto the estimated signal subspace, or alternatively, onto the estimated noise-only subspace (i.e., the orthogonal complement of the estimated signal subspace), which are obtained from the measured EEG or MEG data. The subspace methods that have been studied for MEG/EEG include classic MUSIC (MUltiple SIgnal Classification) and recursive types of MUSIC: e.g., R-MUSIC (Mosher and Leahy 1999) and RAP-MUSIC (Mosher and Leahy 1999). Unfortunately, MUSIC typically provides biased estimates when sources are weak or highly correlated (e.g., Xu and Buckley 1992).
Background noise is referred to as brain noise, which is generated by spontaneous brain activities that cannot be suppressed or reduced by recording systems. Because of the inner generation mechanism for the noise, its appearance on all channels of recording system is spatially correlated. In contrast, uncorrelated noise can be introduced through the recording electrodes. The localization accuracy and spatial resolvability of the MUSIC algorithm is decreased in the presence of spatially correlated brain noise. The noise covariance incorporation treatment of MUSIC algorithm in (Sekihara et al., 1997) is one possible way to deal with such problem, but the required noise covariance matrix is unknown in most cases or at best difficult to be estimated in many experimental conditions.
Accordingly, there is a need for a new method for improving the spatial resolution of source localization in the brain.